|
|
ola-abstract.shtml
Lic-presentation Per Nyblom
Dynamic Abstraction for Interleaved Task Planning and Execution
It is often beneficial for an autonomous agent that operates in a
complex environment to make use of different types of mathematical
models to keep track of unobservable parts of the world or to
perform prediction, planning and other types of reasoning. Since a
model is always a simplification of something else, there always exists
a tradeoff between the model's accuracy and feasibility when it is used
within a certain application due to the limited available computational
resources. Currently, this tradeoff is to a large extent balanced by
humans for model construction in general and for autonomous agents in
particular. This thesis investigates different solutions where such
agents are more responsible for balancing the tradeoff for models
themselves in the context of interleaved task planning and plan
execution. The necessary components for an autonomous agent that
performs its abstractions and constructs planning models dynamically
during task planning and execution are investigated and a method called
DARE is developed that is a template for handling the possible
situations that can occur such as the rise of unsuitable abstractions
and need for dyna mic construction of abstraction levels.
Implementations of DARE are presented in two case studies where both a
fully and partially observable stochastic domain are used, motivated by
research with Unmanned Aircraft Systems. The case studies also
demonstrate possible ways to perform dynamic abstraction and problem
model construction in practice.
|
|
 |
 |
|
